DECISION CHARACTERISTICS, EXTENT OF SCANNING AND INFORMATION
PROCESSING CAPACITY RELATIONSHIPS: IMPACT ON INVESTMENT DECISION
MAKING QUALITY
Nik Maheran Nik Muhammad
Universiti Teknologi Mara, Kelantan
nmaheran@kelantan.uitm.edu.my
Norlina Kamudin
norlina@eau.edu.my
Filzah Md Isa
Universiti Utara Malaysia
filzah@uum.edu.my
Abstract
Successful decision-making strategies emerged from a decision making process where individuals
and organizations seek out and process the information effectively in situations of uncertainties.
Hence, decision success requires a keen strategic understanding of external influences, capacity
to process the information and the characteristics of the decision itself. While this research
focuses on strategic investment decision making, the first question arise concerning whether the
decision characteristics influence the quality of decision made and the extent of environmental
scanning behavior of the decision maker? Secondly, will the extent of scanning done give rise to
quality of investment decision? And does the information processing capacity (IPC) enhance the
relationship between environmental scanning and quality investment decision? Decision
characteristics that comprises of decision task (i.e. complexity, difficulty, familiarity and
ambiguity of the decision) and decision situation (i.e. time pressures, irreversibility and
significance of the decisions) may affect the amount of information required to resolve the
decision taken. Therefore to make quality decision in the highly competitive environment today,
decision makers in any organization need to devote a significant amount of information to
managerial decision making. Environmental scanning is one tool in an organization’s arsenal
that can be used to gain this understanding. It is especially helpful when decision is complex
and requires a large body of information. Getting the right information to the right person at the
right time to produce the right processed information is also of critical importance. Thus, the
adaptation of technology, skills and knowledge synthesis within environmental scanning should
occur. Hypothesis testing method of research design was used in this present study. A crosssectional survey through personal contact and personally distributed questionnaires was carried
out to the CEOs and higher level managers of all industries located all over Malaysia. The
usable questionnaire of 118 collected via convenient sampling was used for the analysis of the
study. The result shows that decision characteristics influence the quality of decision but does
not impact scanning behavior of the CEO and higher level managers.
Keywords: Environmental scanning, decision quality, nature of decision, information processing
capacity
1.0
INTRODUCTION
1
Today's corporate world is undergoing unprecedented changes. The accelerating pace of
technology, markets integration, and highly competitive market, place an increasing
demand to get strategic investment decision right. Malaysia like the rest of the world is
doubling its efforts in transforming the economy towards achieving higher valueadded growth. Therefore, more efficient decision mechanisms are required to support this
transformation. Several studies such as Daft and Weick (1984), Hambrick (1981), and
Venkatraman (1989) found a positive relationship between scanning and performance.
According to Dess, (1987) environmental scanning is the primary strategy and is
necessary in establishing organizational goals. In addition, it has been found that
successful firms differ from unsuccessful firms because they do more scanning and they
also have a broader pattern of scanning (Daft, Sormunen, & Parks, 1988) as scanning will
help decision makers to make better decision and ultimately quality decision.
Decision characteristics on the other hand also impact quality of decisions as
according to Rajagopalan, Rasheed, and Datta (1993), relationship exists between
decision characteristics and the decision making process. The more difficult, irreversible
and risky the decision, the more scanning is needed to achieve quality decision. Leonard,
Scholl and Beauvais (2005) added that differences in decision task and decision situation
can be attributed to decision making processes or the environment where the decision
was made. Thus, quality decision-making in a highly competitive environment today,
needs a significant amount of information to resolve different characteristics of the
decision. Environmental scanning is one tool in an organization’s arsenal that can be
used to gain this information. It is especially true when decision is complex and requires
a large body of information. Therefore, getting the right information to the right person
at the right time to produce the right processed information is of critical importance.
Thus, the adaptation of technology, skills and knowledge synthesis within environmental
scanning should occur.
Similar research on environmental scanning and investment decision-making
were found done by Eknem (2005). However his study looked at how investment
decision was made and mainly descriptive in nature. Another study was found done by
Leroy and Bernard (2004), who posited environmental scanning as a moderator to
enhance productive investment decision and reduce the risk-averse attitude of the
managers. Hence, in the knowledge of the researchers, no study was found to directly
relate whether decision characteristics influence decision quality and scanning behavior
and moreover to comprehensively analyze how managers scan the environment, the
source of information they use, the sector of environment they seek out, and the influence
of information processing capacity to enhance their investment decision-making quality.
Therefore, this research aims to fill such gap in the research of strategic decision making
and environmental scanning by investigate how scanning is done by the top management
in all types of firms in Malaysia, and how it should be done to ensure quality decisions.
2.0
LITERATURE REVIEW
2.1
Decision Characteristics
Decision Characteristics encompasses of decision task, decision situations and individual
indifference (Haris, 1998).
2
Decision Task
Decision task would include the dimensions of complexity of the task, difficulty and
familiarity of the task and ambiguity of the task (Leonard et al., 2005). Empirical
findings suggest that an increase in decision time when the task is unfamiliar or
ambiguous, and also an increase in the amount of information needed when the task is
complex or difficult. According to Wood (1986), complex tasks require significantly
more processing of information cues (where the cues are interrelated to decision task)
than simple tasks.
Decision Situation/Environment
Characteristics of the decision situation or decision environment include time pressures,
irreversibility and significance of the decision and accountability of the decision makers.
Time pressures on the decision will lead to a structured, rule based decision process that
will reduce the number of alternatives generated and considered. Irreversibility of the
decision and significance of the decision and accountability of the decision maker are
linked to an increase in decision time (Abelson & Levi, 1985).
2.2
Decision making theory
In strategic decision making model, many theorists see the analysis of the decision
making processes as the key to understanding how organizations function. For a long
time, literature on decision making was dominated by the assumption that decision
making could take place in an entirely rational way. The rationalist perspective, which
was developed in the 1950s and 1960s, has its roots in Weber’s sociological theory in
which he sees the rationalization of decision making within bureaucratic structures as the
dominant approach to organization (Weber, 1947 in Nilsson and Dalkman, 2001). Simon
(1957) introduced rational decision theory into organization theory. He said that the
decision making process is the core of all organization theory, which should therefore
address questions such as “How are decisions made?” and “How can decisions be made
more rationally?” According to rationality theory, the decision making process is goaloriented and rational. However, the rational Model or “economic man” is the ‘ideal’
model for decision making, but it is not practical, because of the limitations in human
information processing capability and the ability to predict all alternatives which is
termed as ‘Bounded rationality’ by Simon (1957).
The concept of bounded rationality suggests that individuals have perceptual and
information-processing limits. Although managers may want to act rationally, they must
accept the limits. This limited function includes acting upon sufficient rather than
complete knowledge. Hence, there’s a tendency for managers to use simple rather than
complex search strategies for problems and consistently using shortcuts (Miller &
Ireland, 2005). Judgmental perspective of decision making was introduced by Simon in
the late 50s. “Intuitive” decision making is the type of decision-making that involves
interpersonal interaction (Simon, 1987). It relates to irrational or judgmental decisionmaking that involves the behavior and emotions of the decision maker.
The need for quick decisions, the need to cope with demands created by complex
market forces, and the assumed benefit of applying deeply held knowledge, create strong
perceived value for the intuitive approach. However, Miller and Ireland (2005) in their
3
study conclude that, drawing from the evidence of behavioral decision making, strategic
decision making, and mental modeling; intuition is a troublesome decision tool unless it
is combined with more orderly sequential analysis of the situation. Many studies also
found that decision-making styles involve an intimate combination of the two kinds of
decision making, analytical and intuitive (e.g. Simon, 1987; Miller and Ireland, 2005).
2.3 Extent of Environmental scanning
Efforts by executives or decision makers to assess uncertainty and identify opportunities
in their environment are called “extent of scanning behavior”. As environmental changes
have increased in their rapidity, scanning has become one of the most important duties
for executives. Many literatures documented that scanning is used for a variety of
strategic purposes; to reduce uncertainty in the environment (Elenkov, 1997; May et al.,
2000), to achieve competitive advantage through superior information gathering (e.g.
Beal, 2000; Kumar, et al., 2001), to gain knowledge about stakeholder priorities and
demands that can be used to develop effective response strategy (e.g. Kumar et al., 2001),
and to develop strategies that improve financial performance (e.g. Kumar and
Subramaniam, 1998; Venkatraman, 1989). Hambrick (1982) was the first who set the
methodological archetype in measuring scanning behavior. He identifies three behavioral
dimensions of scanning; frequency of scanning, managerial/organizational interest in
scanning and time devoted to scanning activity. Researchers after Hambrick (1982) have
tried to empirically identify other dimensions of scanning behavior in order to measure
how scanning was being conducted. Most researchers looked at source or mode of
scanning, and method of scanning. Those attributes are the conceptualization of the
extent of environmental scanning behavior.
Based on Aguilar’s work, Daft and Weick (1984) and Weick and Daft (1983), has
developed a general model of organizational scanning behavior looking at two
dimension; analyzability ('can we analyze what is happening in the environment’?) and
intrusiveness ('do we intrude actively into the environment to collect information?'). Daft
and Weick (1984) suggest that organizations differ in their modes of scanning, depending
on management's beliefs about the analyzability of the external environment, and the
extent to which the organization intrudes into the environment to understand it. An
organization that believes the environment to be analyzable, in which events and
processes are determinable and measurable, might seek to discover the 'correct'
interpretation through systematic information gathering and analysis. Conversely, an
organization that perceives the environment to be unanalyzable might create or enact
what it believes to be a reasonable interpretation that can explain past behavior and
suggest future actions (Choo, 2001). According to Choo (2001), besides environmental
uncertainty, the level of knowledge and information available about the environment may
also be an important factor for choosing the scanning approach. Scanning approach can
be passive, active, formal or informal and an organization that intrudes actively into the
environment is one that allocates substantial resources for information search and for
testing or manipulating the environment. A passive organization on the other hand takes
whatever environmental information comes its way, and tries to interpret the environment
with the given information. Usually it involves informal method of scanning and relies
more on personal sources of information.
4
Amount of Scanning
The amount of scanning done by managers is one of the common dimensions of scanning
behavior used in earlier studies (Elenkov, 1997; May et al., 2000; Sawyer, 1993). Most
of the previous research conceptualized amount of environment scan based on frequency
of scanning, interest in scanning and the time spend in scanning the environment (e.g.
Ebrahimi, 2000; Elenkov, 1997; Hambrick, 1982; May et al., 2000). However, for the
present study, the concept of the amount of scanning is operationalized by measuring the
amount of information the managers scan on each type of information needed for the
decision they have made.
Method of Scanning
According to many researchers, the method of scanning will impact the quality of
decisions made (e.g. Subramanian, Fernandez, and Harper, 1993). The popular
dimension used to measure method of scanning is formal versus informal systems and
regular versus irregular basis. The formal system consists of a specialized unit and
personnel dedicated to the tasks of acquiring, interpreting, and internally communicating
information on different aspects of the firm’s environment (May et al., 2000). With
formal system, scanning is usually done on routine or regular basis where the information
will be stored and used whenever needed. An informal system is built around the day-today scanning activities of individual managers and is done on an ad-hoc basis by either
middle or top level executives in the organization. The information that is obtained on a
non-routine or informal basis and it is usually gained through chance encounters that do
not seek comprehensive hard data. Information seeking is thus casual and opportunistic,
relying more on irregular contacts and casual information from external, people sources
(Choo, 2000).
Sources of Scanning
Another dimension of environmental scanning behavior is sources of scanning used.
Many literatures have identified several sources of information that can be drawn on in
the scanning process. Typically, the sources can be categorized as internal and external
sources and personal and impersonal sources (Aguilar, 1967). Internal sources of
information about the external environment includes memos, output from management
information systems, and direct contact with managers and employees within the
organization (McGee & Sawyer, 2003), while external sources of information includes
trade publications, direct contact with customers, suppliers, and executives from other
companies as well as attendance at industry-related meetings and seminars as well at the
internet (Wood, 1997). Personal and impersonal sources on the other hand, are
operationalized by looking at the types of contacts. Personal sources of information
originate from personal contacts involving direct communication with other individuals
either within or outside of the organization, such as friends, family members, and close
business associates. On the other hand, impersonal sources originate from non-personal
sources typically written communications such as formal reports, trade publications,
newspapers, government reports, output from management information system and etc.
(Aguilar, 1967; Daft & Weick, 1984).
5
2.4 Information Processing Capacity
Information processing approaches to modeling organizations have been extensively
developed in organization theory (Daft & Weick, 1984; Egelhoff, 1988, 1991, 1992;
Galbraith, 1973; Huber, 1991; Tushman & Nadler, 1978), but this work has had little
influence on the environmental scanning literature. Although a great deal of exploratory
research exist on environmental scanning, it is generally difficult to integrate them with
information processing capacity because there is lack of underlying theoretical
framework that facilitates both situations. Organizational information processing, views
organizations as a system that need to balance the organization’s information processing
capacities against the information-processing requirements inherent in its strategy and
environment. It encompasses organizational structure and organizational design. Given
the high level of uncertainty, complex decisions and the shortage of useful information,
there is often considerable need for “cognitive elaboration” on the part of decision maker
that is skill and experience and decision support systems (Egelhoff & Sen, 1992).
Information processing in organization is generally defined as data gathering,
transformation of data and communicating data into information. An organization
processes information to make sense of its environment, to create new knowledge, and to
make decisions (Choo, 1998). Information processing is also defined as how the
information is modified so that it eventually influences the decision making. Most
empirical research identifies four dimensions of IPC, namely organizational structure
(e.g. Galbraith, 1973; Ochi, 1981; Wiliamson, 1981;) decision experience and skill (e.g.
Flynn and Flynn, 1999; Kraiger, 1988; Levitt and March, 1988; Nass, 1994) managerial
style (e.g. Driver et al.,1996); and decision support systems (e.g. Culnan and Markus,
1988; Daft and Lengel, 1986; King, 2006). However, since the main focus of the study
is on the impact of scanning on decision making quality and not on the manager or the
organization, only two dimensions is appropriate to be used, that is decision experience
and skill and decision support system brought to bear on the decision made.
Decision Experience, Knowledge Skill and Decision Support System
IPC can be operationalized by looking at decision experience and skill - that is the ability
of each person to intelligently process information to provide useful knowledge for
decision-making. This skill and ability is dependent on individual/group experience.
Experienced administrators will have greater levels of knowledge and skill from learning
by doing as they have been exposed more frequently to the intelligence of the
organization.
As the amount of information increased and the firm’s environment and the
decision become more complex, automation and the use of information technology is
needed to make it possible to efficiently analyze the data and trends and process
information accurately on the timely basis.
2.5 Investment Decision Quality
In measuring or evaluating capital investment decision, there are many techniques
currently being discussed in the literature such as traditional mainstream techniques
(discounting cash-flow (DCF) and payback period) which are said to be inadequate in
certain situations especially for the evaluation of investment in research and development
6
(R&D) and technological innovations (Ekanem, 2005). Both situations are intangible in
nature in terms of the benefits involved and the environmental uncertainties that need to
be dealt with (Thomas, 2001). Study by Demeyer, Nakane, Miller, and Ferdows (1989),
found considerable evidence available to support the claim that the financial appraisal
methods (e.g DCF, payback) used by industry to evaluate capital investments may be
inappropriate for today’s high technology business environment. A manufacturing
strategy survey of senior managers of large manufacturers in Europe, North America and
Japan revealed that the competitive priorities of those executives are dimensions of cost
(productivity), quality, flexibility, and dependability/delivery (Demeyer et al., 1989).
Other surveys (e.g. Proctor and Canada. 1992) also indicate product quality as a top
priority in America. According to Proctor and Canada, other less tangible benefits most
widely cited are: (1) improved competitive position, (2) increased manufacturing
flexibility, (3) reduced delivery time, and (4) reduced product development time.
Therefore, strategic evaluation of capital investment which used to be performed by top
management beyond the range of financial appraisal are now brought in line with
justification of investment in advanced manufacturing system and other high technology,
long-range capitalization projects.
Leroy and Bernard (2004) on the other hand added that in an uncertain
environment, that is when the parameters that influenced the future states of nature are
unforeseeable, the question is not so much whether it is profitable to invest but whether it
is opportune to invest immediately instead of waiting. According to them, “decision is
not whether to invest, but also when to invest”. Environmental scanning is a tool that can
be used to identify the opportunities and prospects available.
Quality investment decision is a decision that: (1) meets (or contributes to the
achievement) the objectives of the organization; and (2) gives rise to positive outcome to
the decision maker. Therefore investment decision quality is operationally defined by
looking at decision-making outcome in differentiating between good decision and bad
decision and whether it has met the goal and objective of the organization. To measure
investment decision quality, the respondents were asked to evaluate their specific
investment decision that they have selected and rate how they perceive the outcome of
the decision that they have made, whether it has met their objectives or not.
3.0
THEORETICAL FRAMEWORK AND RESEARCH METHODOLOGY
The theoretical framework of the current study has five major relationships. It is
decision characteristics as independent variables, environmental scanning as intervening
variables, information processing capacity as moderating variables and decision quality
as dependent variable. The framework started by exploring the relationship between
decision characteristics and decision quality. The second part of the analysis examined
the relationship between decision characteristics and environmental scanning. The third
part of the analysis examined the mediating effect of environmental scanning and
decision quality, the fourth analysis examined the decision characteristics and the
inclusion of the mediating variable of environmental scanning with decision quality and
the final analysis is on the moderating effect of Information Processing Capacity on the
environmental scanning and decision quality The five main hypotheses of this study
attempt to answer the central research issue of whether the characteristics of the decision
7
influence the decision quality and whether it is mediated by the scanning behavior of the
decision maker. Furthermore the current study also interested to know if information
processing capacity enhances the environmental scanning behavior and the decision
quality.
The data in the current study relates to capital investment decision, which
therefore forms our unit of analysis. Capital investment involves decision that relates to
the purchase of plant, machinery, building, business, market expansion and product
development. Therefore, information about specific decision made by the decision maker
is crucial for this study. The respondents were asked to choose only one specific decision
that they have made within the last two years. This specific decision that they have
selected will be the main focus of the analysis. The quality of the decision by the
respondents depends on two factors. One is the importance of achieving each of the
listed objectives, and secondly the extent the objectives were achieved. The Decision
Quality Index (DQI) measure used is a weighted sum of the achieved objectives, where
the weights were represented by the importance attached to the each of the objectives.
We measured the importance (rank 1 to 10) by forming the weights attached to each of
the quality. Since the ranks differ from decision to decision, the quality of the decision
was rated based upon the respondent's importance attached. Since the weights summed
up to 55 (10+9+8+7+6+5+4+3+2+1 = 55), the denominator is 55. Therefore the Quality
index formula is as below:
DQI 
 (importance

 (importance
of objective i x achievemen t of objective i)
 importance
of objective i
of objective i x achievemen t of objective i)
55
This unit of analysis is also chosen to enhance internal validity as choosing the
manager or the organization making the decisions will only confuse the issues to be
addressed. This is due to the fact that managers and organizations make many decisions,
some would involve a great amount of scanning, and some none at all, and the associated
quality is high for some and low in others. Therefore, isolating the focus on a specific
decision, measuring the associated scanning behavior and quality, will strengthen the
validity of the relationship thus established.
The population of the study is the investment decision per se which is in the
researcher point of view, cannot be easily identified. It is due to the fact that the
decisions were made by almost every executive. Due to the unidentified population and
sampling frame as well as the nature of the study itself, the most appropriate sampling
method to be used was convenient sampling. The data was obtained from various
sources based on the researcher’s personal contact and networking.
According to
McGrath (2001), the major advantage of using personal contacts and the promise of
useful feed back was that, the respondents were professionally interested in the results
and committed to making sure the data were accurate. Moreover, convenient sampling is
also used commonly in many marketing and strategic management studies (e.g.
Annamalai, 2006; Chan, 2005; Jaworski and Kohli, 1993; McGrath, 2001; Syed
Mohamed, 2004). Based on that argument, the researcher has the confidence to proceed
8
with the convenient sampling although the generalizability of the findings might be
limited as it may be valid only for the sample we have obtained. Therefore the 345
questionnaires were sent to the decision maker of all industry sector all over Malaysia.
4.0
FINDINGS
Various levels of decision makers who make capital investment decisions in the last two
years from various types of companies were analyzed. Out of 345 questionnaires
distributed, 126 were received and 118 were usable. Thus, the total response rate was
37%. The achieved response rate compares favorably to other studies in environmental
scanning conducted in other countries, where it ranges from 17% to 30% only (e.g. Choo,
1993; May et al., 2000).
4.1
Sample Profile
Respondent profile
In terms of who made the decision, the majority of the decision-makers in our sample
hold managerial position (39%) designated in most of the business units such as, regional
manager, branch manager, operation manager, financial manager and etc. followed by
CEOs (33%). 58% of them have a Bachelor’s degree and about 32% holds Masters and
Doctorate degrees. The majority of them have management and business background,
but there are also a significant number of respondents with IT and engineering
background. Thus, we can conclude that the respondents are sufficiently well versed
with their company operations and are able to comprehend the needs of the questionnaire.
In terms of their experience, the majority of the decisions are made by
respondents who have been in the industry for more than 6 years (32%). The important
trend is that slightly more than 50% of them have more than 10 years of experience in the
industry. Although 65% of the respondents have been in their position for less than 5
years, most of them have been with their respective companies for more than 6 years
(40%). It can be seen here that generally the decisions involved in this study are made by
people with long tenure and vast experience in their respective businesses and companies.
Company Profile
In terms of where the sampled decisions are made, 64% are made in the services sector,
which includes trading, tourism, property development, construction, plantation, finance
company, telecommunication industry, education and government agency; while the
remaining 36% are made in the manufacturing sector, such as automotive, petroleum, gas
and biotechnology. Various types of companies are expected to be more representative
of the scanning behavior among Malaysian firms.
Furthermore, 45% of the sampled decisions are made in companies that have been
in operation for more than 15 years but there are also an equal proportion of those which
have been in operation between one to fifteen years. Thus, our sampled decisions are
appropriate for the current study to investigate the environmental scanning behavior in
relation to their investment decision quality. In terms of the size of the companies that
made our sampled decisions, there are also a sufficient number of small and large
responding companies. About 34% are small enterprises and 30% are moderate in size.
9
The remaining 36% are large companies with the number of employees exceeding 500,
lending credence to subsequent results from analysis.
Decision Profile
It was argued that different decisions need different types of information involving
different methods and sources. Therefore, it is crucial to scrutinize the decision profile
as it might point towards different scanning behavior. The data shows that most of the
decisions in the sample are related to capital acquisitions (35%) involving decisions to
acquire plant, machinery, building, land, computers, and etc., and 28% are related to
decisions about research and development, developing new product and new market and
etc. Another 22% are decisions related to business acquisition and mergers while 14%
are related to market expansion. Hence, the study covers a whole spectrum of decisions
which hopefully will reflect the various types of scanning behavior.
The size of investment involved in the decisions was seen to be more on the low
end, with 40% involving an investment of RM1 million or less, and only 20% are of
investments value RM20 million or more.
Decision Objective
Table 1 displays the importance of the objectives of the capital investment decisions
involved in this study. Improving profitability through enhancing product quality and
cost efficiency are the three most important objectives of the decisions made. Improving
knowledge development and the dissemination was the least important consideration of
the decisions.
Table 1
Profile of importance of the objectives achieved
Decisions (N=118)
Objective
Improve quality of product
Improve cost efficiency
MEAN RANK
3.84
3.90
Improve production time
Improve productivity
Improve profitability
Enlarge market share
Develop new product
Enhance employee motivation
5.58
4.19
3.61
5.36
7.19
6.70
Increase innovation capacity
7.12
Develop and disseminate knowledge
7.49
Note: Rank of 1 = most important; 10 = least important
In summary, it can be concluded that the sampled decisions in the study are quite varied
not only in terms of its nature (objectives, investment value, etc.) but also in terms of
where and who made these decisions. It therefore provides useful basis for the subsequent
analysis and inference.
10
4.3
Descriptive analysis
Overall Descriptive Analysis
The main objective of the survey is to have a broad overview of how investment decision
was made among Malaysian decision makers. Decision makers use various information
sources and various scanning methods for decision making and different decision
characteristics will lead to different level of decision quality. Descriptive statistics for the
final list of variables of the study are shown in Table 2.
Table 2
Descriptive statistics of Moderating, Control and Dependent variables
Decision N =118
Variables
Mean
X1: Decision situation
X2 : Decision task
Y1: Extent of scanning
Y2: Method of scanning
Y3: Source of scanning (Personal/Impersonal)
Y4: Source of scanning (External/Internal)
M: Information Processing Capacity
Z: Decision quality
Notes:
X
Y1
Y2
Y3
Y4
M
Z
3.36
2.90
3.32
3.46
2.97
2.93
3.87
3.73
Std.
Deviation
0.67
1.38
0.76
0.62
0.65
0.64
0.61
0.71
scale range:1 (less complex) to 5 (very complex)
scale range: 1(not at all) to 5 (great amount)
scale range:1 (informal) to 5 (formal)
scale range: 1(impersonal) to 5 (personal)
scale range: 1(external) to 5 (internal)
scale range: 1(low) to 5 (high)
scale range: 1(low) to 5 (high)
In gaining insight into how and how much of the business environment was scanned
when making investment decisions, the overall sample was examined. The mean level
of environment being scanned and its dimensions are slightly above 3 indicating a
moderate level of scanning behavior (mean = 3.32). Similarly, the methods used to scan
the environment are moderately formalized (mean = 3.46). Thus, one can conclude that
a combination of both formal and informal methods was used to scan the environment.
Sources used to gather information indicates that slightly more impersonal and external
sources was used to gain the information.
For the level of information processing capacity brought to bear on the decisions,
the complexity of the decision made and the extent of the decision achieved its
objectives, the result shows that decisions made by the decision maker having high
capacity in processing the information (mean = 3.87), the characteristics of the decision
(decision situation/decision task) was perceived to be moderate (mean=3.3) and the
quality decision was rated high with the average mean of 3.73.
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4.4 Validity and Reliability test
Result from the preliminary analyses to determine the goodness of data on the
independent, mediating and moderating variables obtained from the application of factor
analysis and scale reliability testing gave satisfactory output.
4.5
Multiple Regression analysis
Determinants of Decision Characteristics, Extent of Scanning and Decision Quality
Overall, the model depicting the framework representing the interrelationships between
the study variables is strongly supported by the data for extent of scanning behavior and
information processing capacity acting as the moderator to decision quality. It explained
a high proportion of the variations in the investment decision quality. However, for
decision characteristics, the results concluded that a model does not exist as no significant
relationships were found between the variables. Table 3 below depicted the result.
Table 3: Multiple regression: Contextual factors and extent of environmental scanning behavior
(Beta Coefficient)
X1 Decision Task
X2 Decision Situation
Extent
.054
-.036
Mediating Variables
Method
Source(P/IP)
.009
-.074
-.075
.000
Source (I/E)
.011
-.091
R
R square
F value
.064
.004
.233
.075
.006
.329
.091
.008
.484
Independent variables
.074
.005
.313
With regards to extent of scanning behavior, result of regression analysis indicates that
extent of scanning was significant and has positive relationship with investment decision
quality. Further, the higher beta value shows that amount of scanning has significant
impact in explaining the variance in decision quality. Its positive direction indicates that
the more amount of scanning done; the better will be the quality of decision. Method of
scanning and sources of scanning either personal/impersonal or external/internal on the
other hand shows no significant relationships. Therefore, what ever method or sources
used to get the information, will not influence the quality of the decision. The most
important factors influencing decision quality is only how much information being
sought (see Table 4).
Table 4: Multiple Regressions: Extent of environmental scanning and quality of decision
Mediating Variables
Decision Quality
Extent of Scanning
Method of Scanning
Source of scanning (Personal/Impersonal)
Source of Scanning (External/internal)
R
R square
F value
.264***
.172
-.128
-.020
.422
.178
6.108***
***significant at the .001 level ** significant at the .05 level * significant at the 0.1 level
12
Mediating Effect of Extent of Scanning, Method of Scanning and Sources of scanning to
Decision Quality
Our framework posits that the extent of environmental scanning does not mediate the
relationships between contextual factors and the quality of the decision although the
mediating variables affect the dependent variable. This is due to Baron and Kenny’s
condition (1986) that the independent variable must affect mediating variable and the
dependent variables in order to hold the mediation effect. Table 5 to 8 depicts the result
of mediating effect. Therefore, the level of decision complexity either task or situation
does not influence the amount, method and sources of scanning. Furthermore, it will not
also influence the decision made.
Table 5: Hierarchical Regression : Mediating of extent of scanning
DECISION QUALITY
Independent
Unstandardized
R square
R square
Effect
variables
Beta
change
Step 1
Step 2
Decision task
.018
.008
.001
No effect
Extent
.350***
.141***
.139
Decision situation
.127
.140
.014
No effect
Extent
.355***
.158***
.143
***significant at the .001 level ** significant at the .05 level * significant at the 0.1 level
Table 6: Hierarchical Regression : Mediating of method of scanning
DECISION QUALITY
Independent
Unstandardized
R square
R square
Effect
variables
Beta
change
Step 1
Step 2
Decision task
.018
.017
.001
No effect
Method
.339***
.090***
.089
Decision situation
.127
.151
.014
No effect
Method
.352***
.109***
.095
***significant at the .001 level ** significant at the .05 level * significant at the 0.1 level
Table 7: Hierarchical Regression : Mediating of source of scanning (Personal/Impersonal)
DECISION QUALITY
Independent
Unstandardized
/mediating
R square
Beta
R square
Effect
variables
change
Step 1
Step 2
Decision task
.018
.011
.001
No effect
Source (P/IP)
-.213**
.039**
.038
Decision situation
.127
.126
.014
No effect
Source (P/IP)
.214***
.053***
.039
***significant at the .001 level ** significant at the .05 level * significant at the 0.1 level
13
Table 8: Hierarchical Regression : Mediating of sources of scanning (External/Internal)
DECISION QUALITY
Independent/mediating
Unstandardized
R square
variables
Beta
R square
Effect
change
Step 1 Step 2
Decision task
.018
.019
.001
No effect
Source (E/I)
-.150
.019
.018
Decision situation
.127
.115
.014
No effect
Source (E/I)
.138
.029
.015
***significant at the .001 level ** significant at the .05 level * significant at the 0.1 level
Moderating Effect of Extent of Scanning, Method of Scanning and Sources of scanning and
IPC to Decision Quality
To test the moderating effect of information processing capacity on the relationship of
environmental scanning and decision quality, model 2 and 3 display the result of
hierarchical regression analysis (refer to Table 9). The test for moderating influence to
the environmental scanning and quality decision relationship, model 2 upon inclusion of
information processing capacity variable is analyzed. The results indicates that the model
is highly significant (F-change=9.40; p-value=0.003) and the R square improved by
6.4%. With the inclusion of interaction variables in model 3, R square improved only
0.05%, which indicates that the moderating variables have little influence on the
relationship between the extent of scanning and the decision quality.
Hence, the
moderator acts more as a predictor variable with the dependent variable.
The regression coefficient measured by the standardized (β) coefficients indicates
that moderating variables was not significant for the interaction variable of IPC with the
extent of information scan, method and sources used to decision quality. This indicates
that the relationship of extent, method and source with decision quality was not
influenced by the inclusion of moderating variable. Thus, IPC was not a moderator but a
predictor variable.
Table 9: Hierarchical Regression – Environmental scanning and investment decision
making quality
DECISION QUALITY
Model 1
Model 2
Model 3
Model Variable
Extent of scanning
Method of scanning
Source of scanning (P/IP)
Source of scanning(E/I)
Moderating variable
Information Processing Capacity (IPC)
Interaction Variable
IPC_Extent
IPC_ Method
IPC_Source(P/IP)
IPC_Source(E/I)
R square
R square change
F value
F change
.264***
.172
-.128
-.142
.178
.178
6.108***
6.108***
14
.178*
.072
-.065
-.014
-.121
.407
.427
-.871
3.068***
.130
.242
.064
7.132***
9.410***
.423
-.520
-.545
.990
.246
.005
3.923***
.174
5.0
DISCUSSION AND CONCLUSION
The data of the present study found that there are significant and positive relationships
between environmental scanning and investment decision quality. What this means is
that the more scanning is done in making the decisions, the better is the decision made.
However, the analysis suggests that this is only true for amount of scanning done and not
for method and source of scanning when measured simultaneously (i.e multiple
regression). However, when tested the mediating effect of each variables, all the
variables are significant accept for sources (external vs. internal) of scanning; Three
possible reasons why this happened is that; firstly the IV itself has little variation (SD
small) therefore statistically it is impossible for it to explain why the DV varies from unit
to unit; may be due to unit are similar with the IV in the sample questions; Secondly, if
the particular (insignificant IV) is significantly correlated with another IV (that is
significant); in multiple regression, once one variable is significant; the significance of
another IV is evaluated on the additional predictive power that the new IV brings to the
relationship; This situation can be seen by correlation between X1 and X2; in other
words the effect of X2 on Y is subsumed (captured) within X1; much like mediating.
Third possible reason is that the relationship between the IV with Y is non-linear
(suggesting) that the impact of the IV on Y may be moderated by some other variable i.e.
effect of the IV on Y may be contingent on some other factors.
Results of the moderated regression analyses provide no support for hypothesis
concerning the environmental scanning behavior, information processing capacity and
investment decision quality. IPC was found to act as predictor variables and not
moderating variables. This means that IPC does not enhance decision quality, but act as
an influential factor for high quality decision. The reasons for this scenario as explained
by many researchers (e.g. Fahey and King, 1977; nik muhammad et. al 2007) are due to
two reasons. One is either the information is very complex to process or the information
is common knowledge and therefore cannot be used to differentiate between low and
high quality decisions. In both these situations, the need for IPC is minimal. The second
reason is the information may not be relevant to the business decisions involved;
therefore having the capacity to process the information (high IPC) will not make an
impact on turning data into information.
In addition to all the plausible reasons being mentioned, the insignificant results
could also be attributed to a number of influential factors as well. Among the factors that
may influence the level of decision quality are the differential natures of the
organizational culture, structure, leadership style, and business sector or operation of the
organizations involved in the study. Most probably, these factors could directly or
indirectly shape or mold the characteristics of decision; the extent, method, and source of
environmental scanning; and also the information processing capacity of a decision
maker. Since quality of decision is rather subjective in nature, then the way it is being
measured may not be similar. Therefore, further studies should be carried out by taking
into consideration the influential capacity of these factors and other potential factors on
decision quality.
REFERENCES
15
Abelson, R.P. and Levi, A. (1985). Decision making and decision theory. In Lindzey, G.
and Aronson, E (Eds), the handbook of Social Psychology, 3rd Ed. New York:
Random House, 231 – 309.
Aguilar, F.J., (1967). Scanning the business environment. New York: McGraw-Hill.
Beal, R. M., (2000). Competing Effectively: Environmental Scanning, Competitive
Strategy, and Organizational Performance in Small Manufacturing firms. Journal
of Small Business Management, 27-45.
Choo, C.W., (2001). Environmental scanning as information seeking and organizational
learning. Information Research, 7(1). http://InformationR.net/ir/7-1/paper112.html]
Culnan, M.J., (1983). Environment scanning: the effect of task complexity and source
accessibility on information gathering behavior, Decision Science, 14(2), 194-206.
Daft, R. L., and Lengel, R. H., (1986). Organizational Information Requirements, Media
Richness and Structural Design. Management Science 32(5), 554- 571.
Daft, R.L., Sormunen, J. Parks, D., (1988). Chief executive scanning, environmental
characteristics, and company performance: an empirical study.
Strategic
Management Journal 9, 123-139.
Daft, R.L. and Weick, K.E., (1984). Toward a model of organizations as interpretation
systems. Academy of Management Review, 284-295.
Demeyer, A., Nakane, J., Miller, J.G. and K. Ferdows (1989). Flexibility: The Next
Competitive Battle. The Manufacturing Futures Survey," Strategic Management
Journal 10, 26-37.
Dess, G.G., and Davis, P.S., (1984). Porter’s Generic Strategies as Determinants of
Strategic Group Membership and Organizational Performance. Academy of
Management Journal 27, 467-488.
Driver, M.J., Svenson, K., Amoto, R.D., and Pate, L.E. (1996). A Human-InformationProcessing Approach to Strategic Change. Altering Managerial Decision Style.
International Studies of Management and Organization 26(1), 41-58.
Ebrahimi, B.P., (2000). Perceived Strategic Uncertainty and Environmental Scanning
Behavior of Hong Kong Chinese Executives. Journal of Business Research 49(1),
67-78.
Egelhoff, W.G., and Sen, F., (1992). An Information-Processing Model of Crisis
Management. Management Communication Quarterly: McQ (1986-1998), 5(4),
443-484.
Egelhoff, W.G. (1991). Information-Processing theory and the multinational enterprise.
Journal of International Business Studies, 22(3), 341-368.
Egelhoff, W.G. (1982). Strategy and structure in Multinational corporations: An
information processing approach. Administrative Science Quarterly, 27, 435 – 458.
Ekamen, I., (2005). ‘Bootstrapping’: the investment decision-making process in small
firms. The British Accounting Review, 1-2
Elofson, G., and Konsynski, B. (1991). Delegation Technologies: Environmental
Scanning with Intelligent Agents. Journal of Management Information Science, 8(1),
37-62.
Elenkov, D.S. (1997). Strategic uncertainty and environmental scanning: The case for
institutional influence on scanning behavior. Strategic Management Journal, 18,
287-302.
16
Fahey,l. and King, W. (1977).
Business Horizon, 61-71
Environmental Scanning for corporate planning.
Flynn, B.B., and Flynn, E. J., (1999). Information-Processing Alternatives for Coing
with Manufacturing Environment Complexity. Decision Science 30(4), 1021-1051.
Galbraith, J.R. (1973). Designing complex organizations. Reading, MA: AddisonWesley.
Hambrick, D.C. (1981). Specialization of environmental scanning activities among upper
level executives. Journal of Management Studies, 18(3), 299-320
Harris, R., (1998). Decision Making Techniques. http: //www.virtualsalt.com/
crebook6.htm
Huber, G.P., (1991). Organizational Learning: The Contributing process and the
literatures. Organization Science, 2(1), 88 – 115.
King, W.R., (2006). The Critical Role of Information Processing in Creating an Effective
Knowledge Organization. Journal of Database Management 17(1), 1-15.
Kraiger, K., (1988). Implication of Research on Expert/Novice Differences for Training
Assessment and Design. Paper presented at the Annual meeting of the Society of
Industrial/Organizational Psychology, Dallas, TX.
Kumar, K. and Subramaniam, R. (1998). Porter Strategic types: Differences in internal
process and their impact on performance. Journal of Applied Business Research,
14(1), 107-124.
Kumar, K., Subramaniam, R., Strandholm, K., (2001).
Competitive Strategy,
Environmental Scanning and Performance: A Context Specific Analysis of their
Relationship. International Journal of Commerce and Management, 11., 112-142.
Leonard, N.H., Beauvais, L.L., and Scholl, K.W., (2005). A multi-level Model of Group
Cognitive Style in Strategic Decision Making. Journal of Managerial Issues 27(1),
119-138.
Leroy, S., Bernard, J., (2004). Managers and productive investment decisions: the impact
of uncertainty and risk aversion. Journal of Small Business Management.
Levitt, B. and March, J.G., (1988). Organizational Learning. Annual Review of
Sociology, 14, 319 – 340.
May, R.C., Stewart, W.H.J., and Sweo, R., (2000). Environmental scanning behavior in a
transitional economy: evidence from Russia. Academy of Management Journal,
43(3), 403-427.
McGee, J.E.,and Sawyerr, O.O., (2003). Uncertainty and Information Search Activities:
A study of owner-managers of small high-technology manufacturing firms. Journal
of Small Businesses Management 41(4), 385-401.
Miller, C.C., and Ireland, R.D., (2005). Intuition in Strategic Decision Making, Friend or
foe in the fast-paced 21st Century? Academy of Management Executive 19(1), 1930.
Nass, C., (1994). Knowledge or Skills: Which Do Administrators Learn from
Experience?. Organization Science, 5(1), 38-50.
Nillson, M., and Dalkmann, H., (2001). Decision making and Strategic Environment
Assessment. Journal of Environmental Assessment Policy and Management 3(3),
305-327.
17
Ouchi, W.G (1980). Markets, bureaucracies and clans. Administrative Science Quarterly
25 (March), 129 – 41.
Rajagopalan, N., Rasheed, A.M.A., and Datta, D.K. (1993). Strategic Decision
Processes: Critical Review and Future Directions. Journal of Management 2, 349384.
Simon, H.A. (1957). Administration Behavior. Macmillan New York.
Simon, H.A. (1987). Making management decisions: The role of intuition and emotion.
Academy of Management Executive, 1, 57-64.
Sawyerr, O.O., Ebrahimi, B.P., and Thibodeaux, M. S., (2000). Executive Environmental
Scanning, Information Source Utilization, and Firm Performance: the case of
Nigeria. Journal of Applied Management Studies, 9(1), 95-115
Sawyerr, O.O., (1993). Environmental uncertainty and environmental scanning activities
of Nigerian manufacturing executives: a comparative analysis.
Strategic
Management Journal, 14(4), 287-299.
Sawyerr, O.O., Ebrahimi, B.P., Thibodeaux, M.S., (2000). Executive Environmental
Scanning, Information Source Utilization, and Firm Performance: the case of
Nigeria. Journal of applied Management studies 9(1), 96-115.
Subramaniam, R., Fernandez, N., Harper, E., (1993). Environmental scanning in US
companies: their nature and their relationship to performance. Management
International Review, 33(3), 271-286.
Subramaniam, R., Kumar, K., Yauger, C., (1994). The scanning task environments in
hospitals: An empirical study. Journal of Applied Business Research 10(4), 104115.
Tushman, M.L., and Nadler, D.A., (1976). Information Processing as an Integrating
Concept in Organizational Design. Academy of Management Review 3, 613-624.
Venkatraman, N., (1989). Strategic Orientation of Business Entreprises: The Construct,
Dimensionality and Measurement. Management Science 35(8), 942-962.
Weick, K.E., (1988). Enacted sense making in crisis situation. Journal of Management
Studies, 25, 305-317.
Williamson, O.E., (1981). The economic organization: the transaction cost approach.
American Journal of Sociology 87, 548 – 577.
Wood, R. E., (1986). Task complexity: Definition of the construct. Organizational
Behavior and Human Decision Processes 37, 60-82.
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